Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods

Leonhard Gruber; Maria Ledinek; Franz Steininger; Birgit Fuerst-Waltl; Karl Zottl; Martin Royer; Kurt Krimberger; Martin Mayerhofer; Christa Egger-Danner

doi:10.5194/aab-61-413-2018

Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods

Arch Anim Breed. 2018 Oct 30;61(4):413-424. doi: 10.5194/aab-61-413-2018. eCollection 2018.

Authors

Leonhard Gruber^{1

2}, Maria Ledinek^{3

2}, Franz Steininger⁴, Birgit Fuerst-Waltl³, Karl Zottl⁵, Martin Royer¹, Kurt Krimberger¹, Martin Mayerhofer⁴, Christa Egger-Danner⁴

Affiliations

¹ Agricultural Research and Education Centre Raumberg-Gumpenstein, Irdning-Donnersbachtal, 8952, Austria.
² These authors contributed equally to this work.
³ Department of Sustainable Agricultural Systems, BOKU - University of Natural Resources and Life Sciences Vienna, Vienna, 1180, Austria.
⁴ ZuchtData EDV-Dienstleistungen GmbH, Vienna, 1200, Austria.
⁵ LKV Niederösterreich, Zwettl, 3910, Austria.

Abstract

The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (Model $_{HG BG}$ ) reduced RMSE to 32.5 kg, and adding HW reduced it further to 30.4 kg (Model $_{HG BG HW}$ ). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, Model $_{HG BG}$ and Model $_{HG BG HW}$ , were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0 kg (Model $_{HG BG HW}$ , Model $_{HG BG}$ , lactation) and 39.9 and 41.3 kg (Model $_{HG BG HW}$ , Model $_{HG BG}$ , dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6 % of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.